code: def test_ItemSet_Iterable_Only(batch_size, shuffle, drop_last):
      num_ids = 103
  
      class InvalidLength:
          def __iter__(self):
              return iter(torch.arange(0, num_ids))
  
      seed_nodes = gb.ItemSet(InvalidLength())
      item_set = gb.ItemSet(seed_nodes, names="seed_nodes")
      item_sampler = gb.ItemSampler(
          item_set,
          batch_size=batch_size,
          shuffle=shuffle,
          drop_last=drop_last,
      )
      minibatch_ids = []
      for i, minibatch in enumerate(item_sampler):
          assert isinstance(minibatch, gb.MiniBatch)
          assert minibatch.seed_nodes is not None
          assert minibatch.labels is None
          is_last = (i + 1) * batch_size >= num_ids
          if not is_last or num_ids % batch_size == 0:
              assert len(minibatch.seed_nodes) == batch_size
          else:
              if not drop_last:
                  assert (
                      len(minibatch.seed_nodes) == num_ids % batch_size
                  )
              else:
                  assert False
          minibatch_ids.append(minibatch.seed_nodes)
      minibatch_ids = torch.cat(minibatch_ids)
      assert (
          torch.all(minibatch_ids[:-1] <= minibatch_ids[1:])
          is not shuffle
      )
  
imports: import os
  import re
  from sys import platform
  import dgl
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  from dgl import graphbolt
  from torch.testing import assert_close
label_desc: Use image node_id 2 for calling a global function with example usage: test_ItemSet_Iterable_Only(batch_size, shuffle, drop_last) and returns: iter